Transcriptome profiling of the rat retina after optic nerve transection
Bibliographic record
Abstract
Glaucoma is a group of eye diseases characterized by alterations in the contour of the optic nerve head (ONH), with corresponding visual field defects and progressive loss of retinal ganglion cells (RGCs). This progressive RGC death is considered to originate in axonal injury caused by compression of the axon bundles in the ONH. However, the molecular pathomechanisms of axonal injury-induced RGC death are not yet well understood. Here, we used RNA sequencing (RNA-seq) to examine transcriptome changes in rat retinas 2 days after optic nerve transection (ONT), and then used computational techniques to predict the resulting alterations in the transcriptional regulatory network. RNA-seq revealed 267 differentially expressed genes after ONT, 218 of which were annotated and 49 unannotated. We also identified differentially expressed transcripts, including potentially novel isoforms. An in silico pathway analysis predicted that CREB1 was the most significant upstream regulator. Thus, this study identified genes and pathways that may be involved in the pathomechanisms of axonal injury. We believe that our data should serve as a valuable resource to understand the molecular processes that define axonal injury-driven RGC death and to discover novel therapeutic targets for glaucoma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".